Multiple-scale quantum harmonic oscillator multi-mode function optimization system and method

An optimization method and harmonic oscillator technology, applied in the field of computational intelligence, can solve problems such as easy failure, inability to converge, and inability to realize multiple global optimal position searches, so as to achieve the effect of search

Inactive Publication Date: 2016-05-04
SOUTHWEST UNIVERSITY FOR NATIONALITIES
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Problems solved by technology

[0003] The MQHOA method is designed with the goal of unimodal function optimization. When the objective function has multiple global optimal positions, the existing MQHOA method is prone to failure, that is, the existing MQHOA method can only achieve one global optimal position. Optimization problems of complex functions
Moreover, du

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  • Multiple-scale quantum harmonic oscillator multi-mode function optimization system and method
  • Multiple-scale quantum harmonic oscillator multi-mode function optimization system and method
  • Multiple-scale quantum harmonic oscillator multi-mode function optimization system and method

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[0030] The existing MQHOA method compares the function values ​​of all sampling points in each iteration, and selects the optimal k positions as the new Gaussian sampling center positions to enter the next iteration. The inner loop convergence condition is: when the variance of the center positions of all k Gaussian sampling regions is less than the current scale, the outer scale transformation is entered. This convergence condition is when the objective function has more than one global optimal solution. The center position of the Gaussian sampling area is easy to gather at different global optimal solution positions, so that the variance of these center position coordinates has been unable to meet the convergence condition of the inner loop, and the inner loop cannot achieve convergence. The multi-scale quantum harmonic oscillator multi-mode function optimization method (Multi-scaleQuantumHarmonicOscillatorAlgorithm, MultimodalOptimization) of the present invention improves th...

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Abstract

The invention relates to the calculation intelligent field and particularly relates to a multiple-scale quantum harmonic oscillator multi-mode function optimization system and a method. The invention improves the optimal position selection strategy of the current MQHOA method, all gauss sampling areas perform comparison on function values by targeting the sampling points generated by itself and the position of the optimal value is reserved as a new gauss sampling center. In the meantime, the convergence condition of the innermost loop is changed to the condition where the difference of the variance of all gauss sampling center positions between two iterations is smaller or equal to the current scale, and the QHO (quantum harmonic oscillator iteration ) performs internal layer circulation convergence. In the invention, the essence of the QHO convergence condition is to perform convergence when the change of each gauss sampling area position is small. The invention can realize the optimization problem of the complex function of the multi-global optimal position. For the complex functions having the multi-global optimal position, the method disclosed by the invention can perform convergence in most of time and thus realizes the search for the multiple global optimal positions.

Description

Technical field [0001] The invention relates to the field of computational intelligence, and in particular to a system and method for optimizing multi-scale quantum harmonic oscillators and multi-mode functions. The system and method can be widely used in the fields of industry, economy, science and the like. Background technique [0002] The multi-scale quantum harmonic oscillator optimization method (Multi-scale Quantum Harmonic Oscillator Algorithm, MQHOA) is a computationally intelligent method to solve the unimodal global optimization problem constructed by the probability interpretation of the quantum harmonic oscillator wave function. [0003] The MQHOA method is designed to aim at the unimodal function optimization problem. When the objective function has multiple global optimal positions, the existing MQHOA method is prone to failure, that is, the existing MQHOA method can only achieve a global optimal position. Optimization of complex functions. Moreover, due to the limi...

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Application Information

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IPC IPC(8): G06Q10/04
CPCG06Q10/04
Inventor 王鹏谢千河
Owner SOUTHWEST UNIVERSITY FOR NATIONALITIES
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